深度学习方法下GEDI数据的天然云杉林地上生物量反演

doi:10.13866/j.azr.2023.09.11

Abstract

Abstract:

As the largest carbon reservoir on land, forests play a crucial role in human life and development. Understanding the dynamic changes in forest resources and modernizing their sustainable development is currently a significant research focus. This study focuses on natural Picea forests in the Tianshan Mountains and uses ground measurement data, helicopter airborne LiDAR point cloud data, and Global Ecosystem Dynamics Investigation (GEDI) data to construct a multisource fusion data framework. By utilizing deep learning algorithms within the AutoKeras framework, the study aims to predict the regression model of multiple relative height quantiles of GEDI data and their aboveground biomass in the study area, thereby validating the feasibility of GEDI data for large-scale aboveground biomass retrieval. The main conclusions are as follows: (1) GEDI data are highly feasible for estimating forest aboveground biomass. Through automated deep learning algorithms and training and verification sets, the overall data achieve a coefficient of determination (R²) of 0.69, 0.63, and 0.67, respectively, along with a mean absolute error of 3.73 mg·hm^-2, 4.22 mg·hm^-2, and 3.89 mg·hm^-2, demonstrating high prediction accuracy. (2) Helicopter LiDAR, an intermediate technology for estimating aboveground biomass using GEDI data, exhibits a single tree recognition accuracy of over 0.75 across the study area. The study successfully utilizes multimodal data fusion to quantitatively describe the structural parameters of the single tree foundation in the study area while verifying the potential of GEDI data for obtaining forest aboveground biomass. Moreover, the study provides a theoretical basis for estimating carbon sources and sinks, biomass, stock, forest management, biodiversity protection, and other projects in similar areas, offering essential guidance, and fundamental data support.

Key words: natural Picea forest, GEDI, LiDAR, aboveground biomass, deep learning

SUN Danyang,WEI Jianxin,YANG Liao,WANG Jie,TANG Yuqi,Babierjiang DILIXIATI. Study on using deep learning method to retrieve the biomass of natural Picea forest from GEDI data[J].Arid Zone Research, 2023, 40(9): 1472-1483.

Figures/Tables 13

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网络层	输出大小	参数个数
输入	[（None，81）]	0
多类别编码层	（None，81）	0
归一化层	（None，81）	163
全连接层	（None，64）	5248
激活函数1	（None，64）	0
全连接层	（None，1024）	66560
激活函数2	（None，1024）	0
回归层	（None，1）	1025

评价指标	召回率（R）/%	准确度（P）/%	F值/%
样地1	67.40	93.93	78.48
样地2	78.57	96.70	82.24
样地3	89.40	84.29	86.77
平均值	78.46	88.16	82.50

模型评价指标	数据集	R²	RMSE /(mg·hm^-2)	MAE /(mg·hm^-2)
深度学习（DL）	训练集	0.69	4.68	3.73
	验证集	0.63	5.31	4.22
	总体数据	0.67	4.88	3.89

Study on using deep learning method to retrieve the biomass of natural Picea forest from GEDI data

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